Published by Zhou et al., [1]} introduced another deep detection architecture that removes the need for anchors, inspired by CornerNet [2]}. During the training-set preparation, the authors proposed to draw a map with delta functions located at the center of the ground-truth bounding boxes. Then, they used a Gaussian filter to smear these centers, generating a smooth distribution that reaches a peak at each object center. In the end, for each image, they generated an associated ground truth heatmap. The model then uses two prediction heads: one is trained to predict the confidence heatmap, whereas the other head is trained, like in other objects detectors, to predict regression values for box dimensions and offsets, which refers to the box center predicted by the first head. The more interesting part is perhaps the fact that the confidence heatmap can be directly exploited to remove irrelevant predictions without decoding the boxes and without breaking the deep cnn flow. In particular, the authors proposed running a max-pooling operation on the heatmap, aiming to elevate each cell a little bit, except at the very peak. Then, a small flat region is created very near the peak. Next, they suggested running a boolean element-wise comparison between the max-pool input and output that return the values as FP values (either 0. or 1.), creating a new heatmap where everywhere is 0 (since the max-pool operation elevated each value) except local maxima, where the value is 1. Finally, they proposed to run an element-wise multiplication between the output of this latter stage and the input to the max-pool to return each local maximum to its confidence level and leave the non-maximum values as 0. Like other detectors, a confidence threshold has been applied as the last step, decoding only the boxes corresponding to surviving maxima.
